DATA VISUALIZATION TOOLS

Paper Code: 
25DCAI 704A
Credits: 
06
Periods/week: 
12
Max. Marks: 
100.00
Objective: 

Course Objectives:

The course will enable the students to

1. learn the concepts of data analysis with the tool R.

2. understand the interfaces of power BI and Tableau.

3. design reports and dashboards for data visualization.

 

Course Outcomes: 

Course

Learning outcome

(at course level)

Learning and teaching strategies

Assessment Strategies

Course Code

Course

title

 

25DCAI 704A

 

DATA VISUALIZATION TOOLS

(PRACTICAL)

CO127. Apply analysis tool R to solve practical problems in a variety of disciplines

CO128.  Inspect the basic features of power BI & DAX to perform data analysis

CO129. Create a dashboard using power BI.

CO130. Inspect basic features of Tableau and analyse structured data

CO131. Create static and dynamic charts on datasets using Tableau

CO132.Contribute effectively in course-specific interaction

 

Approach in teaching:

Discussions, Demonstrations

 

Learning activities for the students:

 

Self-learning assignments, Practical questions

Class test, Quiz, Practical Assignments, Semester end examinations

 

R

  • Introduction to R Studio, Logical Arguments, Missing Values, Characters, Factors and Numeric, Help in R, Vector to Matrix, Matrix Access.  Data Frames, Data Frame Access, Basic Data Manipulation Techniques, Usage of various apply functions – apply, lapply, sapply and tapply, Outliers treatment.
  • Charts (Bar, Pie, Histogram), Measures of Central Tendency and Measures of dispersion

Power BI

  •  Power BI ecosystem. its relationship with Excel, Power BI suite of products, integration of Power    BI products and analytics process flow. Differentiate between the various data sources Connect Power BI to a data source, Clean and transform data to ensure data quality, Load the data to the Power BI Data Model.
  • Introduction to DAX, apply DAX basics in Power BI Desktop, Define DAX formulas, its applications, its syntax rules Create basic calculated columns and measures, Row and Filter Context Apply common DAX expressions such as FILTER, SUM. Page Backgrounds and Templates, create visualizations to display the data, apply drill through and drill down, create and manage slicers with the use of filters. explore visual interactions, Publish the report to the Power BI Service

Tableau

  • Introduction to Tableau, Evaluation of Tableau, Tableau Architecture and Installation of Tableau. Data analysis and data communication with Tableau. Tableau public and desktop. Knowing Your Data, importing data, the “Data Visualization Process”, table view, Dashboard, Tableau Basics. Dimensions, Measures, Tableau Workspace, cards and shelves, marks card, formatting how to analyse structured data using Tableau, exporting data
  •  Creating Static and Dynamic Charts in Tableau: How to create simple static charts in Tableau, selecting appropriate chart, bar charts, line charts, bubble charts, scatter charts, tree maps, stacked bar charts, bulleted charts and histogram Visualizing locations and time, reshape data using Tableau. Preparing elevator pitch. Measures. sum, average, median, minimum, maximum, standard deviation and variance, removing outliers, Filters and groups, data hierarchies, trend lines, box plot and regression.

 

ESSENTIAL READINGS: 

SUGGESTED TEXT BOOKS:

  1. Srivasa K.G., Siddesh G M,Shetty,” Statistical Programming in R”, Oxford University Press 2017
  2. Cole Nussbaumer Knaflic, ”Storytelling with Data. A Data Visualization Guide for Business Profesionals”, Wiley,2015.
  3.  Daniel G. Murray  ,"Tableau Your Data!. Fast and Easy Visual Analysis with Tableau Software", Wiley,2016.
  4. Nathan Yau," Data Points", Wiley,2013.
  5. Lindy Ryan,"Visual Data Storytelling with Tableau ", Pearson Addison-Wesley Data & Analytics,2018.
  6. Jose Berengueres  , Ali Fenwick and  Marybeth Sandell ,"Introduction to Data Visualization & Storytelling. A Guide For The Data Scientist " , Stokes-Hamilton (29 July 2019)

 

REFERENCES: 

SUGGESTED REFERENCE BOOKS:

  1. Cotton, Richard(2016) Learning R: A Step-by-Step Function Guide to Data Analysis
  2. Lindy Ryan,"Visual Data Storytelling with Tableau ", Pearson Addison-Wesley Data & Analytics,2018.
  3. Jose Berengueres  , Ali Fenwick and  Marybeth Sandell ,"Introduction to Data Visualization &      Storytelling. A Guide for The Data Scientist “, Stokes-Hamilton (29 July 2019)

      REFERENCE JOURNALS:

  1. International Journal of Data Mining, Modelling and Management, Inderscience:  https://www.inderscience.com/jhome.php?jcode=ijdmmm
  2. Data Mining and Knowledge Discovery, Springer : https://www.springer.com/journal/10618

e-RESOURCES INCLUDING LINKS:

  1. R Tool Spoken Tutorial: https://spoken-tutorial.org/
  2. Tableau official website tutorials:https.//www.tableau.com/
  3. My Great Learning Courses on data visualization :https.//www.mygreatlearning.com/academy/learn-for-           free/courses/data-visualization-using-tableau

 

 

Academic Year: 
2025-26
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